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The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic process. Therefore, a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this paper. Firstly, the partial least squares (PLS) algorithm is used to select key input variables as a preprocessing step. Then, a recurrent fuzzy neural network model is...
The Fuzzy Nearest Neighbor time series forecasting technique (FNN), compiles a time series into a set of fuzzy rules capable of inferring the next value, based on the current observation. The training phase compiles all different scenarios of what has been observed in the past as a set of fuzzy rules. FNN has been tested against other methodologies, showing a satisfactory performance. This article...
The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach...
In recent years, an intelligent micro-grid system composed of renewable energy sources is becoming one of the interesting research topics. The success design of daily load forecasting enables the intelligent micro-grid system to manipulate an optimized loading and unloading control by measuring the electrical supply for achieving the best economical and power efficiency. In this study, intelligent...
Ionospheric forecasting is a popular research area required by telecommunication and navigation system planners and operators. The problem is challenging because ionospheric processes are nonlinear. Data-driven techniques are of particular interest since they overcome most of these difficulties. In this work, two possible ionospheric forecasting approaches have been considered to be employed along...
Due to the lack of natural resources, the majority of energy in many countries must depend on import, and the corresponding cost is expensive and affected by international market fluctuation and control. In recent years, an intelligent micro-grid system composed of renewable energy sources is becoming one of the interesting research topics. The forecasting of short-term loads enables the intelligent...
the annealing furnace temperature control system in this paper is designed to enhance anti-disturbances and improve the performance and accuracy of temperature controlling for the system. Considering the controlled object's characteristics of nonlinear, large hysteresis, time-varying namely uncertainty, the authors have used the method of wavelet function being adopted in neural network prediction...
The dam monitoring modal based on cluster algorithm fuzzy neural network with fuzzy rule and neural network is presented. An example is given of the analysis of the horizontal displacement of dam. The result shows that the system is more precise than the regression modal.
Creating an applicable and precise financial early warning model is highly desirable for decision makers and regulators in the financial industry. Although Business Failure Prediction (BFP) especially banks has been extensively a researched area since late 1960s, the next critical step which is the decision making support scheme has been ignored. This paper presents a novel model for financial warning...
The prediction of exploitable reserves of oil layer is a complicated problem, which involves many geological and crude oil parameters. Considering its intrinsic properties, this paper put forward an improved fuzzy neural network (FFN) method, and compared it with the traditional BP method. The results showed that this method has better accuracy and reliability, hence it may provide an important reference...
This paper presented a new prediction model for material property (strength of materials for gray cast iron) based on composition and microstructure using a recent learning algorithm called Sensitivity Based Linear learning Method (SBLLM). This method was proposed in order to address the problems of back propagation learning algorithm for feed forward neural network. Thus we have made use of this...
In this paper, through combining information diffusion principle and BP neural network theory, a new prediction model of drought disaster assessment is established. First, the original data are fuzzily processed based on information diffusion method, then a new training sample is formed; second, the new sample is used to design and train BP neural network; finally, the trained fuzzy neural network...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
Taking Zhengzhou-Xi'an passenger dedicated line as an example, based on the analysis of the main influencing factors, a fuzzy neural networks model for predicting seismic subsidence coefficient of loess subgrade has been established. The model combines the fuzzy information optimization technology and neural network. It integrates the two theories, by making up the defects of the neural network in...
The self-adaptation and self-studying features of neural networks have been combined with the logical reasoning ability of fuzzy system to produce a fuzzy system based neural networks, BP arithmetic is used to adjust system parameters. Finally the fuzzy neural networks model with the wind farms calculation is established, as compared with the model of multi-variant linear regression, the fuzzy neural...
A new type of breakout prediction system based on multilevel neural network for continuous casting was proposed, which consists of a pattern recognition unit of single-thermocouple temperature pattern based on BP neural network, a logic judgment unit of multi-thermocouple temperature pattern and a decision making unit of fuzzy neural network based on T-S (Takagi-Sugeno) model. In the training of BP...
Convergence speed of the traditional BP neural network is slow, and it is easy to fall into local minimum. A novel dynamic recurrent fuzzy neural network model is proposed, which is used to resolve the power system short-term load forecasting. The fuzzy inference function is realized easily by using a product operation in the network. The simulation results indicate that the proposed network can overcome...
Aimed to the measuring problem of steam consumption in Dyeing process, a multiple neural network soft sensing modeling of Dyeing steam consumption based on adaptive fuzzy C-means clustering (FCM) is presented. The method is used for separating a whole real-time training data set into several clusters with different centers, and the clustering centers can been modified by an adaptive fuzzy clustering...
Tape Rectification is a highly nonlinear process. Conventional Model Predictive Control (MPC) may not be able to cope with such process. The Fuzzy Neural Network (FNN) methodology is proposed to realize the model of the nonlinear process. The measured data serve as samples to train the neural network. The prediction accuracy is guaranteed due to the interpolation ability of the feedforward network...
Based on the rule derived from the Rough Sets methodology, we designed the fuzzy neural networks. Using the regular parameter and the default value that are estimated by the discretization results, the network is up to an optimal value rapidly by large amount of training. When applied to model the solvent dehydrating tower in the PTA complex process, the performance is superior to the common feed-forward...
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